Project Summary

Project Abstract:
Drivers encounter various road conditions such as merging traffics, construction zones, etc. on a daily basis. Work zone and traffic controls are mandated by the United States Department of Transportation standards. They are designed to provide a safe and efficient travel for the drivers. However, these standards can be improved further. The purpose of this study is to investigate the interactions between driver characteristics and behavior with traffic conditions. The objectives of this study are: 1) to determine the effects of traffic patterns and traffic flow levels on driver behavior, 2) to demonstrate the use of human factors analysis techniques applied to the understanding of driver behavior and performance. To achieve the objectives of this study, the LSU driving simulator will be used to gather performance data from 20 drivers to determine how individual differences and individual responses to driving conditions (perceived workload and situation awareness, etc.) shape driving behavior.

Project Objectives:
The proposed research will investigate the interactions between driver characteristics and behavior with traffic conditions using a full-size driving simulator combined with human factors analysis techniques. Understanding driver behavior is critical to improving the safety of roadways, particularly in construction zones, high-traffic areas, and evacuation scenarios. Specifically, this research will address two major objectives:

To determine the effects of traffic patterns and traffic flow levels on driver behavior

To demonstrate the use of human factors analysis techniques applied to the understanding of driver behavior and performance

Task Descriptions:Task 1: Modify driving simulation to model a highway construction zone
The driver will navigate through a simulated construction zone. The factors tested in this construction zone will be lane merges and traffic congestion level. There will be two types of lane merges: conventional and joint (two-sided taper), and two levels of congestion: high and low.

Task 2: Conduct a driving simulation experiment
Using the simulation developed in Task 1, twenty students at LSU will be recruited to participate in an experiment to test their behavior and responses to various driving conditions. Each participant will experience all combinations of merge type (conventional and joint) and traffic level (high and low).

Driver performance will be measured with standard data from the driving simulator such as lane deviation, speed, braking patterns, and success in completing the driving tasks. Behavior of the drivers will be recorded using digital cameras and possibly an eye tracking device. Performance-shaping variables including perceived workload and situation awareness will be recorded to determine if there is any correlation between these and driving performance. Finally, individual differences in driving style, personality type (A or B), gender, age, and driving experience will be recorded to further analyze the performance data.

Participants’ perceived workload will be measured in several dimensions (mental workload, physical workload, temporal workload, frustration, performance, and effort) using the NASA-TLX (Task Load Index). Participants mark a continuous scale from low to high on each dimension at the end of the experiment to reflect their workload.

Situation awareness (SA) measures the level of awareness a person has of his/her environment at three levels: perception, comprehension, and projection. SA has been correlated with performance in related fields such as aviation and maritime navigation. Using SAGAT (Situation Awareness Global Assessment Technique), participants are interrupted for short periods of time during the simulation to answer questions on SA at all three levels of awareness.

Type A or Type B tendencies are a popular personality type description. In addition to describing personality, these tendencies have been linked with risk for heart disease and increased muscle activation. It is hypothesized that Type A personalities will be more aggressive drivers. This personality characteristic will be measured using the 20-item Jenkins student activity survey.

Task 3: Analyze human factors data
Statistical analysis will include descriptive statistics of performance, ANCOVA to determine differences in driving conditions on performance when individual differences are considered, and correlation analysis to determine relationships between performance, performance-shaping metrics, and individual differences. The end product of this analysis will be a description of how individual differences and individual responses to driving conditions (perceived workload and situation awareness) shape driving behavior.

Implementation of Research Outcomes:This project was an attempt to improve traffic flow and drivers’ safety at work zones. The new work zone layout which was studied in this project proved to perform better, in term of improving driving safety and traffic flow, compared to the tradition methods of lane closure near work zones.

Impacts/Benefits of Implementation:This research studied driver safety which is one of the most important issues of transportation in any country. Many people, such as drivers, workers and transportation authorities can benefit from the improvements suggested in this project. Using Joint Lane Merge can reduce the number of accidents and fatalities and improve driving flow which results in less time in traffic and consequently less fuel consumption. The results from this research could also impact DOT’s guidelines for road closure.

It is expected that results of this project will impact other disciplines such as cognitive psychology and cognitive ergonomics. This project studied a new work zone layout and its impact on drivers’ workload. Workload, in this context, is the amount of mental and physical stress that a driver experiences when he/she drives in a certain environment. This notion is widely researched in psychology and is perceived as one of the main factors that affects drivers’ performance.